<p> This study analyses the growth prospects of Pakistan over the period 1978 to 1990. The framework used for the analysis is a macroeconometric model of the economy which is constructed and estimated based on data for the period 1956 to 1978. The predictive ability of the model is evaluated in terms of its ability to forecast values of the endogenous variables in an historic simulation context. The model is then used initially to forecast values of major endogenous variables over the period 1978 to 1990, based on a-benchmark set of assumptions about growth in the exogenous variables. The results of the benchmark forecast are compared with results of six other forecasts in which the assumptions about the future course of key domestic and international factors are varied.</p> <p> The macroeconometric model consists of a system of nonlinear dynamic simultaneous equations. An input-output block composed of ten production sectors is incorporated. The final demand side is disaggregated into private and public expenditures, including exports and imports by economic categories. The revenue structure and role of the government in the development of the economy are treated as endogenously determined. The influence of bank credit on capital formation and the effect of government deficits on the money supply are explicitly incorporated.</p> <p> The results of the initial benchmark forecast with the model are compared with targets set by the Pakistan Planning Commission in the context of the Fifth Five-Year Plan (1978-83). It is found that the plan was consistent but was not feasible with regard to the amount of non-inflationary resources required to carry it out. For these reasons, the plan was aborted in 1981. </p> <p> In the other experiments it is found that a higher international growth rate, though raising the growth path of the Pakistan economy, does not translate into an equivalent increase in growth in the domestic economy. The economy's growth path is found to be very sensitive to the course of agricultural development. A harvest failure in one year would permanently lower the growth path for subsequent years. The economy is found to be flexible enough to withstand a surge in imports in one year without its long-run growth path being affected. Finally, on the basis of the last experiment, a feasible course of action for government policy is suggested. This would require development expenditures to increase by half the amount suggested in the plan. Under this policy, gross domestic product and related aggregates would have annual real growth of 4.1 percent in the long-run accompanied by yearly increases in employment of 3.3 percent. Government and balance of payments deficits would be cut to manageable size, resulting in reduced inflationary tendencies in the economy.</p> / Thesis / Doctor of Philosophy (PhD)

In this paper, we have employed the Kourtellos et al. (2007) threshold model to examine the relationship between inflation and relative price variability in Hong Kong, Argentina, Germany, Japan, Mexico and Philippines. Empirical results from Hong Kong, Japan and Mexico show that inflation are endogenous variables, and the relationship between these two variables appears to be a V shape for Hong Kong and Japan. However, the relationship appears to be positive for Mexico. Empirical results fail to reject the hypothesis of exogenous inflation for Argentina, Germany and Philippines, and the relationship between these two variables appears to be a V shape for Philippines and Argentina. There is no significant relationship between these two variables for Germany.

This paper apply a threshold model to examine the nonlinear relationship between economic growth and unemployment rate. The key innovation of our model is that it takes into account endogenous explanatory variables and endogenous threshold variables. Empirical results show that Okun¡¦s law is nonlinear. Among different models under consideration,the absolute value of Okun¡¦s coefficient during contraction period is larger than that during expansion period.

<p>This dissertation aims to introduce a new sequential Monte Carlo (SMC) based estimation framework for structural models used in macroeconomics and industrial organization. Current Markov chain Monte Carlo (MCMC) estimation methods for structural models suffer from slow Markov chain convergence, which means parameter and state spaces of interest might not be properly explored unless huge numbers of samples are simulated. This could lead to insurmountable computational burdens for the estimation of those structural models that are expensive to solve. In contrast, SMC methods rely on the principle of sequential importance sampling to jointly evolve simulated particles, thus bypassing the dependence on Markov chain convergence altogether. This dissertation will explore the feasibility and the potential benefits to estimating structural models using SMC based methods.</p><p> Chapter 1 casts the structural estimation problem in the form of inference of hidden Markov models and demonstrates with a simple growth model.</p><p> Chapter 2 presents the key ingredients, both conceptual and theoretical, to successful SMC parameter estimation strategies in the context of structural economic models.</p><p> Chapter 3, based on Chen, Petralia and Lopes (2010), develops SMC estimation methods for dynamic stochastic general equilibrium (DSGE) models. SMC algorithms allow a simultaneous filtering of time-varying state vectors and estimation of fixed parameters. We first establish empirical feasibility of the full SMC approach by comparing estimation results from both MCMC batch estimation and SMC on-line estimation on a simple neoclassical growth model. We then estimate a large scale DSGE model for the Euro area developed in Smets and Wouters (2003) with a full SMC approach, and revisit the on-going debate between the merits of reduced form and structural models in the macroeconomics context by performing sequential model assessment between the DSGE model and various VAR/BVAR models.</p><p> Chapter 4 proposes an SMC estimation procedure and show that it readily applies to the estimation of dynamic discrete games with serially correlated endogenous state variables. I apply this estimation procedure to a dynamic oligopolistic game of entry using data from the generic pharmaceutical industry and demonstrate that the proposed SMC method can potentially better explore the parameter posterior space while being more computationally efficient than MCMC estimation. In addition, I show how the unobserved endogenous cost paths could be recovered using particle smoothing, both with and without parameter uncertainty. Parameter estimates obtained using this SMC based method largely concur with earlier findings that spillover effect from market entry is significant and plays an important role in the generic drug industry, but that it might not be as high as previously thought when full model uncertainty is taken into account during estimation.</p> / Dissertation

Effects of human capital in developing countries Bc. Jan Mouček Abstract Improving education and health of people living in developing countries is an often discussed topic. We however know relatively little about the effects that investments into schooling and improving the nutrition and hygiene of people in those countries have on their productivity at work. This thesis investigates the effects of four variables representing investment into human capital on income of individuals from Bangladesh and Indonesia. The four studied variables are education, migration, height and Body Mass Index. The thesis aims at comparing results of estimation using instrumental variables estimator with previous similar research. It tries to observe possible differences in size of effects the human capital variables have. It finds however that the instrumental variables used are of insufficient quality for the estimation to yield reliable results. Because the instruments used were almost the same as in underlying works and their quality has not been previously tested the conclusion of this thesis is that further search for correct instrumental variables describing the human capital variables in various countries around the globe is necessary.

Models of land use change fall into two broad categories: pattern based and process based. This thesis focuses on pattern based land use change models, expanding our understanding of these models in three important ways. First, it is demonstrated that some driving variables do not have a smooth impact on the land use transition process. Our example variable is access to water. Land managers with access to piped water do not have any need for surface or groundwater. For variables like this, a model needs to change the way that driving variables are represented. The second important result is that including a variable which captures spatial correlation between land use types significantly increases the explanatory power of the prediction model. A major weakness of pattern based land use models is their inability to model interactions between neighbouring land parcels; the method proposed in this study can be an alternative to account for spatial neighbourhood association. These innovations are applied using the CLUE-S (Conversion of Land Use and its Effects at Small regional extent) system to the Deep Creek watershed in the Southern Interior of British Columbia. The results highlight the challenge of balancing the protection of agricultural land and conserving forest and natural areas when population and economic growth are inevitable. The results also demonstrate the implications of land use change on existing land use policies. The calibrated model was validated using remote sensing data. A series of discriminant functions were estimated for each land use type in the recent period and these functions were then used to classify. The calibrated model was run in reverse, back to the generated land use classification, and results compared. Fit was reasonable with error rates falling below ten percent when radii beyond 2.5 km were considered. Another important contribution is demonstrating the importance of modelling dynamic variables. Some important drivers are changing continuously and others depend on land use change itself. Failure to update these variables will bias model forecasts. Spatial neighbourhood association, an endogenous variable governed by land use change itself, is again used as the example dynamic variable. The study demonstrates the importance of updating all associated information. / Graduate Studies, College of (Okanagan) / Graduate